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machine learning
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Tag #machine learning

394 papers:

CGOCGO-2020-CowanMCBC #automation #generative #kernel
Automatic generation of high-performance quantized machine learning kernels (MC, TM, TC, JB, LC), pp. 305–316.
ECSAECSA-2019-GalsterGG #perspective #quality #what
What Quality Attributes Can We Find in Product Backlogs? A Machine Learning Perspective (MG, FG, FG), pp. 88–96.
EDMEDM-2019-HuangBS #collaboration #identification #using
Identifying Collaborative Learning States Using Unsupervised Machine Learning on Eye-Tracking, Physiological and Motion Sensor Data (KH, TB, BS).
EDMEDM-2019-JayaramanGG #identification #student #using
Supporting Minority Student Success by using Machine Learning to Identify At-Risk Students (JDJ, SG, JG).
EDMEDM-2019-NandaD #categorisation
Machine Learning Based Decision Support System for Categorizing MOOC Discussion Forum Posts (GN, KAD).
EDMEDM-2019-Woodruff #architecture #education #interactive #predict #student
Predicting student academic outcomes in UK secondary phase education: an architecture for machine learning and user interaction (MW).
ICPCICPC-2019-PecorelliPNL #detection #heuristic #smell
Comparing heuristic and machine learning approaches for metric-based code smell detection (FP, FP, DDN, ADL), pp. 93–104.
ICSMEICSME-2019-KallisSCP #classification
Ticket Tagger: Machine Learning Driven Issue Classification (RK, ADS, GC, SP), pp. 406–409.
MSRMSR-2019-BangashSCWHA #case study #developer #ml #stack overflow #what
What do developers know about machine learning: a study of ML discussions on StackOverflow (AAB, HS, SAC, AWW, AH, KA0), pp. 260–264.
SANERSANER-2019-PupoNENRB #data flow #information management #named
GUARDIAML: Machine Learning-Assisted Dynamic Information Flow Control (ALSP, JN, KE, AN, CDR, EGB), pp. 624–628.
SEFMSEFM-2019-Kawamoto #logic #specification #statistics #towards
Towards Logical Specification of Statistical Machine Learning (YK0), pp. 293–311.
CoGCoG-2019-DiazPF #game studies #interactive
Interactive Machine Learning for More Expressive Game Interactions (CGD, PP, RF), pp. 1–2.
CoGCoG-2019-JohansenPR #game studies #video
Video Game Description Language Environment for Unity Machine Learning Agents (MJ, MP, SR), pp. 1–8.
CIKMCIKM-2019-LuLW0 #database #modelling #similarity #string
Synergy of Database Techniques and Machine Learning Models for String Similarity Search and Join (JL, CL, JW, CL0), pp. 2975–2976.
CIKMCIKM-2019-VazirgiannisNS #graph #kernel
Machine Learning on Graphs with Kernels (MV, GN, GS), pp. 2983–2984.
ICMLICML-2019-BansalLRSW #higher-order #logic #named #proving #theorem proving
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving (KB, SML, MNR, CS, SW), pp. 454–463.
ICMLICML-2019-GhorbaniZ
Data Shapley: Equitable Valuation of Data for Machine Learning (AG, JYZ), pp. 2242–2251.
ICMLICML-2019-KleimanP #metric #modelling #multi #named #performance
AUCμ: A Performance Metric for Multi-Class Machine Learning Models (RK, DP), pp. 3439–3447.
ICMLICML-2019-QiaoAZX #fault tolerance
Fault Tolerance in Iterative-Convergent Machine Learning (AQ, BA, BZ, EPX), pp. 5220–5230.
KDDKDD-2019-AhmedABCCDDEFFG #ml
Machine Learning at Microsoft with ML.NET (ZA, SA, MB, RC, WSC, YD, XD, VE, SF, TF, AG, MH, SI, MI, NK, GK, PL, IM, SM, SM, GN, JO, GO, AP, JP, PR, MZS, MW, SZ, YZ), pp. 2448–2458.
KDDKDD-2019-BernardiME #lessons learnt #modelling
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (LB, TM, PE), pp. 1743–1751.
KDDKDD-2019-BirdHKKM #challenge #lessons learnt
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (SB, BH, KK, EK, MM), pp. 3205–3206.
KDDKDD-2019-Caruana #black box #modelling
Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning (RC), p. 3174.
KDDKDD-2019-Chen0 #data mining #mining #optimisation #order #robust
Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning (PYC, SL0), pp. 3233–3234.
KDDKDD-2019-DongR #integration
Data Integration and Machine Learning: A Natural Synergy (XLD, TR), pp. 3193–3194.
KDDKDD-2019-FauvelMFFT #detection #towards
Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection (KF, VM, ÉF, PF, AT), pp. 3051–3059.
KDDKDD-2019-Guestrin
4 Perspectives in Human-Centered Machine Learning (CG), p. 3162.
KDDKDD-2019-HuNYZ #collaboration #distributed #framework #named
FDML: A Collaborative Machine Learning Framework for Distributed Features (YH, DN, JY, SZ), pp. 2232–2240.
KDDKDD-2019-HwangOCPM
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning (JH, PO, JC, KP, LM), pp. 2325–2335.
KDDKDD-2019-LiakhovichD
Preventing Rhino Poaching through Machine Learning (OL, GDC), p. 3177.
KDDKDD-2019-NetoPPTBMO #approach #health #permutation
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health (ECN, AP, TMP, MT, BMB, LM, LO), pp. 54–64.
KDDKDD-2019-SrivastavaHK #approach
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning (MS, HH, AK), pp. 2459–2468.
KDDKDD-2019-WangLYLLZ0 #approach #nondeterminism #quantifier
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting (BW, JL0, ZY0, HL, TL, YZ0, GZ0), pp. 2087–2095.
MoDELSMoDELS-2019-NguyenRRPI #approach #automation #classification #metamodelling #repository
Automated Classification of Metamodel Repositories: A Machine Learning Approach (PTN, JDR, DDR, AP, LI), pp. 272–282.
OnwardOnward-2019-Allamanis #modelling
The adverse effects of code duplication in machine learning models of code (MA), pp. 143–153.
PLDIPLDI-2019-GopinathGSS #compilation #modelling
Compiling KB-sized machine learning models to tiny IoT devices (SG, NG, VS, RS0), pp. 79–95.
PLDIPLDI-2019-IyerJPRR #synthesis
Synthesis and machine learning for heterogeneous extraction (ASI, MJ, SP, AR, SKR), pp. 301–315.
ASEASE-2019-Balasubramaniam #representation #towards #using
Towards Comprehensible Representation of Controllers using Machine Learning (GB), pp. 1283–1285.
ASEASE-2019-JiangLJ #how #recommendation
Machine Learning Based Recommendation of Method Names: How Far are We (LJ, HL, HJ), pp. 602–614.
ASEASE-2019-Zhang #approach #identification #injection #sql
A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities (KZ), pp. 1286–1288.
ESEC-FSEESEC-FSE-2019-AggarwalLNDS #black box #modelling #testing
Black box fairness testing of machine learning models (AA, PL, SN, KD, DS), pp. 625–635.
ESEC-FSEESEC-FSE-2019-FucciMM #api #documentation #identification #on the #using
On using machine learning to identify knowledge in API reference documentation (DF, AM, WM), pp. 109–119.
ESEC-FSEESEC-FSE-2019-Moghadam #performance #testing
Machine learning-assisted performance testing (MHM), pp. 1187–1189.
ESEC-FSEESEC-FSE-2019-MostaeenSRRS #named #validation
CloneCognition: machine learning based code clone validation tool (GM, JS, BR, CKR, KAS), pp. 1105–1109.
ASPLOSASPLOS-2019-AnkitHCNFWFHS0M #named #programmable
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference (AA, IEH, SRC, GN, MF, RSW, PF, WmWH, JPS, KR0, DSM), pp. 715–731.
CASECASE-2019-ChenLVR #process #using
Strip Snap Analytics in Cold Rolling Process Using Machine Learning (ZC, YL, AVM, FR), pp. 368–373.
CASECASE-2019-KoWNL #information management
Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing (HK, PW, NYN, YL), pp. 648–654.
CASECASE-2019-LauerL #predict
Plan instability prediction by machine learning in master production planning (TL, SL), pp. 703–708.
CASECASE-2019-MatsuokaNT #approach #identification #problem #scheduling
Machine Learning Approach for Identification of Objective Function in Production Scheduling Problems (YM, TN, KT), pp. 679–684.
CGOCGO-2019-Castro-LopezL #compilation #deployment #modelling #multi
Multi-target Compiler for the Deployment of Machine Learning Models (OCL, IFVL), pp. 280–281.
ICSTICST-2019-KahlesTHJ #agile #analysis #automation #testing
Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments (JK, JT, TH, AJ), pp. 379–390.
ICSTICST-2019-KocWFCP #assessment #empirical #java #static analysis
An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool (UK, SW, JSF, MC, AAP), pp. 288–299.
ICSTICST-2019-SharmaW #algorithm #testing
Testing Machine Learning Algorithms for Balanced Data Usage (AS, HW), pp. 125–135.
ICTSSICTSS-2019-AichernigB0HPRR #behaviour #hybrid #modelling #testing
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (BKA, RB, ME0, MH, FP, WR, AR, MT, MT), pp. 3–21.
ICTSSICTSS-2019-NakajimaC #dataset #generative #source code #testing
Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs (SN0, TYC), pp. 56–64.
JCDLJCDL-2018-Nielsen #library
Introduction to Machine Learning for Digital Library Applications (RDN), pp. 421–422.
JCDLJCDL-2018-TkaczykCSB #evaluation #open source #parsing
Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers (DT, AC, PS, JB), pp. 99–108.
ICSMEICSME-2018-MillsEH #automation #classification #maintenance #traceability
Automatic Traceability Maintenance via Machine Learning Classification (CM, JEA, SH), pp. 369–380.
MSRMSR-2018-BraiekKA08 #framework
The open-closed principle of modern machine learning frameworks (HBB, FK, BA), pp. 353–363.
MSRMSR-2018-BulmerMD #developer #ide #predict
Predicting developers' IDE commands with machine learning (TB, LM, DED), pp. 82–85.
SANERSANER-2018-NucciPTSL #detection #question #smell #using
Detecting code smells using machine learning techniques: Are we there yet? (DDN, FP, DAT, AS, ADL), pp. 612–621.
SCAMSCAM-2018-MostaeenSRRS #automation #design #research #tool support #towards #using #validation
[Research Paper] On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic Code Clone Validation Tools (GM, JS, BR, CKR, KAS), pp. 155–164.
CIKMCIKM-2018-DingLX0S #optimisation #realtime
Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability (YD, JL, JX, MJ0, YS), pp. 2147–2154.
ICMLICML-2018-BollapragadaMNS
A Progressive Batching L-BFGS Method for Machine Learning (RB, DM, JN, HJMS, PTPT), pp. 619–628.
ICMLICML-2018-DamaskinosMGPT
Asynchronous Byzantine Machine Learning (the case of SGD) (GD, EMEM, RG, RP, MT), pp. 1153–1162.
ICMLICML-2018-KallusZ
Residual Unfairness in Fair Machine Learning from Prejudiced Data (NK, AZ), pp. 2444–2453.
ICMLICML-2018-LiuDRSH
Delayed Impact of Fair Machine Learning (LTL, SD, ER, MS, MH), pp. 3156–3164.
ICMLICML-2018-ZadikMS #orthogonal
Orthogonal Machine Learning: Power and Limitations (IZ, LWM, VS), pp. 5723–5731.
KDDKDD-2018-AckermannWUNRLB #framework #modelling #policy
Deploying Machine Learning Models for Public Policy: A Framework (KA, JW, ADU, HN, ANR, SJL, JB, MD, CC, LH, RG), pp. 15–22.
KDDKDD-2018-BeeckMSVD #predict
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion (TODB, WM, KS0, BV, JD), pp. 606–615.
KDDKDD-2018-Fan #approach
The Pinterest Approach to Machine Learning (LF), p. 2870.
KDDKDD-2018-KumarRBVWKEFMZG #using
Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks (AK, SAAR, BB, RAV, KHW, CK, SE, AF, AM, JZ, RG), pp. 472–480.
KDDKDD-2018-RouxPMVF #approach #detection #using
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (DdR, BP, AM, MDPV, CF), pp. 215–222.
KDDKDD-2018-StaarDAB #corpus #documentation #framework #platform #scalability
Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale (PWJS, MD, CA, CB), pp. 774–782.
KDDKDD-2018-Xing #algorithm #co-evolution #design #named
SysML: On System and Algorithm Co-design for Practical Machine Learning (EPX), p. 2880.
ESEC-FSEESEC-FSE-2018-HuZY #named #reuse #robust #testing #user interface #using
AppFlow: using machine learning to synthesize robust, reusable UI tests (GH, LZ, JY), pp. 269–282.
CCCC-2018-Shen #compilation
Rethinking compilers in the rise of machine learning and AI (keynote) (XS), p. 1.
ICSTICST-2018-KhosrowjerdiMR #approach #injection #testing
Virtualized-Fault Injection Testing: A Machine Learning Approach (HK, KM, AR), pp. 297–308.
ECSAECSA-2017-BhatSBHM #approach #automation #design
Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach (MB, KS, AB, UH, FM), pp. 138–154.
EDMEDM-2017-BalyanMM #approach #comprehension #natural language
Combining Machine Learning and Natural Language Processing Approach to Assess Literary Text Comprehension (RB, KSM, DSM).
ICSMEICSME-2017-WangWW17a #recognition #semantics
Semantics-Aware Machine Learning for Function Recognition in Binary Code (SW0, PW0, DW), pp. 388–398.
AIIDEAIIDE-2017-SnodgrassSO #generative
Studying the Effects of Training Data on Machine Learning-Based Procedural Content Generation (SS, AS, SO), pp. 122–128.
CIKMCIKM-2017-KimPP #modelling #performance
Machine Learning based Performance Modeling of Flash SSDs (JK, JP, SP), pp. 2135–2138.
CIKMCIKM-2017-LiHPG #detection #framework #named
DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection (YL, DH, AP, ZG), pp. 1559–1567.
CIKMCIKM-2017-Rastogi
Machine Learning @ Amazon (RR), p. 1.
ICMLICML-2017-KumarGV #internet #ram
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things (AK, SG, MV), pp. 1935–1944.
ICMLICML-2017-NguyenLST #named #novel #probability #problem #recursion #using
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient (LMN, JL, KS, MT), pp. 2613–2621.
ICMLICML-2017-SelsamLD
Developing Bug-Free Machine Learning Systems With Formal Mathematics (DS, PL, DLD), pp. 3047–3056.
KDDKDD-2017-AndersonM #classification
Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity (BA, DAM), pp. 1723–1732.
KDDKDD-2017-BaylorBCFFHHIJK #framework #named #platform
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (DB, EB, HTC, NF, CYF, ZH, SH, MI, VJ, LK0, CYK, LL, CM, ANM, NP, SR, SR0, SEW, MW, JW, XZ, MZ), pp. 1387–1395.
KDDKDD-2017-Bloom #industrial
Industrial Machine Learning (JB), p. 13.
KDDKDD-2017-ChengHHIMPRSSST #flexibility #framework
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks (HTC, ZH, LH, MI, CM, IP, GR, DS, JS, DS, YT, PT, MW, CX, JX), pp. 1763–1771.
KDDKDD-2017-KarpatneK #big data #challenge
Big Data in Climate: Opportunities and Challenges for Machine Learning (AK, VK), pp. 21–22.
KDDKDD-2017-Pafka #question
Machine Learning Software in Practice: Quo Vadis? (SP), p. 25.
KDDKDD-2017-RistovskiGHT #integration #optimisation #simulation
Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines (KR, CG0, KH, HKT), pp. 1981–1989.
KDDKDD-2017-SalehianHL #approach #crowdsourcing #scalability
Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach (HS, PDH, CL), pp. 2001–2009.
KDDKDD-2017-SharmaSKS #problem
The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue (AS, VS, VK, LS), pp. 2011–2019.
MoDELSMoDELS-2017-HartmannMFT #domain model #evolution #integration #modelling
The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling (TH, AM, FF, YLT), p. 180.
ASEASE-2017-GodefroidPS #fuzzing
Learn&Fuzz: machine learning for input fuzzing (PG, HP, RS), pp. 50–59.
ESEC-FSEESEC-FSE-2017-MaAXLZLZ #algorithm #graph #named
LAMP: data provenance for graph based machine learning algorithms through derivative computation (SM, YA, ZX, WCL, JZ, YL, XZ0), pp. 786–797.
ICSE-2017-VendomeVBPGP #detection #exception #open source
Machine learning-based detection of open source license exceptions (CV, MLV, GB, MDP, DMG, DP), pp. 118–129.
ICSMEICSME-2016-GopinathWHK #fault #using
Repairing Intricate Faults in Code Using Machine Learning and Path Exploration (DG, KW, JH, SK), pp. 453–457.
CoGCIG-2016-DeboeverieRAVP #classification #game studies #gesture
Human gesture classification by brute-force machine learning for exergaming in physiotherapy (FD, SR, GA, PV, WP), pp. 1–7.
CIKMCIKM-2016-GuoXYHLLGC #data flow #process
Ease the Process of Machine Learning with Dataflow (TG, JX0, XY, JH, PL, ZL, JG, XC), pp. 2437–2440.
CIKMCIKM-2016-Najork #email #experience #using
Using Machine Learning to Improve the Email Experience (MN), p. 891.
ICMLICML-2016-LibertyLS
Stratified Sampling Meets Machine Learning (EL, KJL, KS), pp. 2320–2329.
ICPRICPR-2016-AginakoMRLS #approach #difference
Machine Learning approach to dissimilarity computation: Iris matching (NA, JMMO, IRR, EL, BS), pp. 170–175.
ICPRICPR-2016-GordonL #modelling
Exposing and modeling underlying mechanisms in ALS with machine learning (JG0, BL), pp. 2168–2173.
ICPRICPR-2016-KrompAWBDBGTAH #framework #image
Machine learning framework incorporating expert knowledge in tissue image annotation (FK, IA, TW, DB, HD, MB, TG, STM, PA, AH), pp. 343–348.
KDDKDD-2016-Chayes #estimation #modelling #network
Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks (JTC), p. 1.
KDDKDD-2016-ChenH0 #web
Lifelong Machine Learning and Computer Reading the Web (ZC0, ERHJ, BL0), pp. 2117–2118.
KDDKDD-2016-Downs #how
How Machine Learning has Finally Solved Wanamaker's Dilemma (OD), p. 405.
KDDKDD-2016-HaarenSDF #using
Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques (JVH, HBS, JD, PF), pp. 627–634.
KDDKDD-2016-Mierswa #workflow
The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows (IM), p. 411.
KDDKDD-2016-RendleFSS #in the cloud #robust #scalability
Robust Large-Scale Machine Learning in the Cloud (SR, DF, EJS, BYS), pp. 1125–1134.
KDDKDD-2016-SinghSA #independence #using
Question Independent Grading using Machine Learning: The Case of Computer Program Grading (GS, SS, VA), pp. 263–272.
KDDKDD-2016-Srivastava #scalability #theory and practice
Large-Scale Machine Learning at Verizon: Theory and Applications (AS), p. 417.
KDDKDD-2016-TaghaviLK #memory management #recommendation #using
Compute Job Memory Recommender System Using Machine Learning (TT, ML, YK), pp. 609–616.
ASEASE-2016-LiLQHBYCL #constraints #execution #symbolic computation #theorem proving
Symbolic execution of complex program driven by machine learning based constraint solving (XL, YL, HQ, YQH, LB, YY, XC, XL), pp. 554–559.
SLESLE-2016-ParrV #towards
Towards a universal code formatter through machine learning (TP, JJV), pp. 137–151.
DocEngDocEng-2015-SilvaFLCOSR #automation #documentation #summary
Automatic Text Document Summarization Based on Machine Learning (GPeS, RF, RDL, LdSC, HO, SJS, MR), pp. 191–194.
SIGMODSIGMOD-2015-HuangBTRTR #scalability
Resource Elasticity for Large-Scale Machine Learning (BH, MB, YT, BR, ST, FRR), pp. 137–152.
SIGMODSIGMOD-2015-ReABCJKR #database #question
Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? (CR, DA, MB, MIC, MIJ, TK, RR), pp. 283–284.
TPDLTPDL-2015-Nunzio #education #geometry #naive bayes
Teaching Machine Learning: A Geometric View of Naïve Bayes (GMDN), pp. 343–346.
VLDBVLDB-2015-KumarJYNP #normalisation #optimisation
Demonstration of Santoku: Optimizing Machine Learning over Normalized Data (AK, MJ, BY, JFN, JMP), pp. 1864–1875.
EDMEDM-2015-AlexandronZP #approach #student
Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly - A Machine Learning Approach (GA, QZ, DEP), pp. 520–523.
SIGITESIGITE-2015-YeraSLHSG
Work In Progress: Machine Learning In Robotics (GY, AS, HL, TH, CS, TG), p. 105.
CoGCIG-2015-KarpovJM #behaviour
Evaluating team behaviors constructed with human-guided machine learning (IVK, LMJ, RM), pp. 292–298.
CHICHI-2015-AmershiCDLSS #analysis #named #performance #tool support
ModelTracker: Redesigning Performance Analysis Tools for Machine Learning (SA, MC, SMD, BL, PYS, JS), pp. 337–346.
CHICHI-2015-KatanGF #development #interactive #interface #people #using
Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People (SK, MG, RF), pp. 251–254.
CSCWCSCW-2015-ChengB #classification #hybrid #named
Flock: Hybrid Crowd-Machine Learning Classifiers (JC, MSB), pp. 600–611.
ICMLICML-2015-BlumH #contest #reliability
The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
KDDKDD-2015-Agarwal #scalability #statistics #web
Scaling Machine Learning and Statistics for Web Applications (DA), p. 1621.
KDDKDD-2015-Athey #evaluation #policy
Machine Learning and Causal Inference for Policy Evaluation (SA), pp. 5–6.
KDDKDD-2015-Durrant-Whyte
Data, Knowledge and Discovery: Machine Learning meets Natural Science (HDW), p. 7.
KDDKDD-2015-Gomez-Rodriguez #modelling #network #probability #problem #research #social
Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods (MGR, LS), pp. 2315–2316.
KDDKDD-2015-LakkarajuASMBGA #framework #identification #student
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes (HL, EA, CS, DM, NB, RG, KLA), pp. 1909–1918.
KDDKDD-2015-Pratt #predict #protocol #proving
Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts (KBP), pp. 2049–2058.
KDDKDD-2015-Schleier-Smith #agile #architecture #realtime
An Architecture for Agile Machine Learning in Real-Time Applications (JSS), pp. 2059–2068.
KDDKDD-2015-SethiYRVR #classification #scalability #using
Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery (MS, YY, AR, RRV, SR), pp. 2069–2078.
KDDKDD-2015-ShashidharPA
Spoken English Grading: Machine Learning with Crowd Intelligence (VS, NP, VA), pp. 2089–2097.
KDDKDD-2015-XingHDKWLZXKY #big data #distributed #framework #named #platform
Petuum: A New Platform for Distributed Machine Learning on Big Data (EPX, QH, WD, JKK, JW, SL, XZ, PX, AK, YY), pp. 1335–1344.
RecSysRecSys-2015-HuD #recommendation #scalability
Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
SEKESEKE-2015-SaputriL #analysis #perspective
Are We Living in a Happy Country: An Analysis of National Happiness from Machine Learning Perspective (TRDS, SWL), pp. 174–177.
SACSAC-2015-FauconnierKR #approach #recognition #taxonomy
A supervised machine learning approach for taxonomic relation recognition through non-linear enumerative structures (JPF, MK, BR), pp. 423–425.
SACSAC-2015-NascimentoPM #algorithm #metaheuristic
A data quality-aware cloud service based on metaheuristic and machine learning provisioning algorithms (DCN, CESP, DGM), pp. 1696–1703.
ASPLOSASPLOS-2015-LiuCLZZTFZC #named
PuDianNao: A Polyvalent Machine Learning Accelerator (DFL, TC, SL, JZ, SZ, OT, XF, XZ, YC), pp. 369–381.
CASECASE-2015-FarhanPWL #algorithm #predict #using
Predicting individual thermal comfort using machine learning algorithms (AAF, KRP, BW, PBL), pp. 708–713.
CASECASE-2015-SrinivasanBSSR #automation #modelling #network #using
Modelling time-varying delays in networked automation systems with heterogeneous networks using machine learning techniques (SS, FB, GS, BS, SR), pp. 362–368.
CASECASE-2015-SundarkumarRNG #api #detection #modelling #topic
Malware detection via API calls, topic models and machine learning (GGS, VR, IN, VG), pp. 1212–1217.
CASECASE-2015-SustoM #approach #multi #predict
Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
DACDAC-2015-VenkataramaniRL #classification #energy
Scalable-effort classifiers for energy-efficient machine learning (SV, AR, JL, MS), p. 6.
DATEDATE-2015-ZhuM #linear #optimisation #programming #using
Optimizing dynamic trace signal selection using machine learning and linear programming (CSZ, SM), pp. 1289–1292.
HPCAHPCA-2015-WuGLJC #estimation #performance #using
GPGPU performance and power estimation using machine learning (GYW, JLG, AL, NJ, DC), pp. 564–576.
PPoPPPPoPP-2015-AshariTBRCKS #kernel #on the #optimisation
On optimizing machine learning workloads via kernel fusion (AA, ST, MB, BR, KC, JK, PS), pp. 173–182.
DRRDRR-2014-MaXA #algorithm #segmentation #video
A machine learning based lecture video segmentation and indexing algorithm (DM, BX, GA), p. ?–8.
SIGMODSIGMOD-2014-CaiGLPVJ #algorithm #comparison #implementation #platform #scalability
A comparison of platforms for implementing and running very large scale machine learning algorithms (ZC, ZJG, SL, LLP, ZV, CMJ), pp. 1371–1382.
VLDBVLDB-2014-BoehmTRSTBV #hybrid #parallel #scalability
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML (MB, ST, BR, PS, YT, DB, SV), pp. 553–564.
VLDBVLDB-2014-SunRYD #classification #crowdsourcing #named #scalability #using
Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing (CS, NR, FY, AD), pp. 1529–1540.
CHICHI-2014-KuleszaACFC #concept #evolution
Structured labeling for facilitating concept evolution in machine learning (TK, SA, RC, DF, DXC), pp. 3075–3084.
HCIDUXU-DI-2014-GencerBZV #detection #mobile
Detection of Churned and Retained Users with Machine Learning Methods for Mobile Applications (MG, GB, ÖZ, TV), pp. 234–245.
ICEISICEIS-v1-2014-ShakirIB #topic
Machine Learning Techniques for Topic Spotting (NS, EI, ISB), pp. 450–455.
ICMLICML-c2-2014-HuS #multi #predict
Multi-period Trading Prediction Markets with Connections to Machine Learning (JH, AJS), pp. 1773–1781.
ICPRICPR-2014-AodhaSBTGJ #interactive
Putting the Scientist in the Loop — Accelerating Scientific Progress with Interactive Machine Learning (OMA, VS, GJB, MT, MAG, KEJ), pp. 9–17.
ICPRICPR-2014-BayramogluKEANKH #approach #detection #image #using
Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach (NB, MK, LE, MA, MN, JK, JH), pp. 3345–3350.
ICPRICPR-2014-MontagnerjH
A Machine Learning Based Method for Staff Removal (IdSM, RHJ, NSTH), pp. 3162–3167.
KDDKDD-2014-Mullainathan #question #social
Bugbears or legitimate threats?: (social) scientists’ criticisms of machine learning? (SM), p. 4.
KDDKDD-2014-Rudin #algorithm
Algorithms for interpretable machine learning (CR), p. 1519.
KDDKDD-2014-SrikantA #programming #using
A system to grade computer programming skills using machine learning (SS, VA), pp. 1887–1896.
KDIRKDIR-2014-Bleiweiss #execution #using
SoC Processor Discovery for Program Execution Matching Using Unsupervised Machine Learning (AB), pp. 192–201.
SEKESEKE-2014-SinghS #requirements #using
Software Requirement Prioritization using Machine Learning (DS, AS), pp. 701–704.
FSEFSE-2014-Joseph #framework #interactive
Software programmer management: a machine learning and human computer interaction framework for optimal task assignment (HRJ), pp. 826–828.
ICSEICSE-2014-LeeJP #behaviour #detection #memory management #modelling #using
Detecting memory leaks through introspective dynamic behavior modelling using machine learning (SL, CJ, SP), pp. 814–824.
CASECASE-2014-KernWGBM #estimation #using
COD and NH4-N estimation in the inflow of Wastewater Treatment Plants using Machine Learning Techniques (PK, CW, DG, MB, SFM), pp. 812–817.
CASECASE-2014-SustoWPZJOM #adaptation #flexibility #maintenance #predict
An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
DACDAC-2014-AlbalawiLL #algorithm #classification #design #fixpoint #implementation #power management
Computer-Aided Design of Machine Learning Algorithm: Training Fixed-Point Classifier for On-Chip Low-Power Implementation (HA, YL, XL), p. 6.
OSDIOSDI-2014-LiAPSAJLSS #distributed #parametricity #scalability
Scaling Distributed Machine Learning with the Parameter Server (ML, DGA, JWP, AJS, AA, VJ, JL, EJS, BYS), pp. 583–598.
DocEngDocEng-2013-Esposito #documentation
Symbolic machine learning methods for historical document processing (FE), pp. 1–2.
ICDARICDAR-2013-TuarobBMG #automation #detection #documentation #pseudo #using
Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning (ST, SB, PM, CLG), pp. 738–742.
SIGMODSIGMOD-2013-CondieMPW #big data
Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
TPDLTPDL-2013-KlampflK #approach
An Unsupervised Machine Learning Approach to Body Text and Table of Contents Extraction from Digital Scientific Articles (SK, RK), pp. 144–155.
VLDBVLDB-2013-BergamaschiGILV #data-driven #database #keyword #named #relational #semantics
QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques (SB, FG, MI, RTL, YV), pp. 1222–1225.
ICSMEICSM-2013-FontanaZMM #approach #detection #smell #towards
Code Smell Detection: Towards a Machine Learning-Based Approach (FAF, MZ, AM, MM), pp. 396–399.
ICSMEICSM-2013-OsmanCP #algorithm #analysis #diagrams
An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams (MHO, MRVC, PvdP), pp. 140–149.
ICSMEICSM-2013-SemenenkoDS #image #named #testing
Browserbite: Accurate Cross-Browser Testing via Machine Learning over Image Features (NS, MD, TS), pp. 528–531.
ICSMEICSM-2013-SorOTS #approach #detection #memory management #statistics #using
Improving Statistical Approach for Memory Leak Detection Using Machine Learning (VS, PO, TT, SNS), pp. 544–547.
CoGCIG-2013-AlayedFN #behaviour #detection #online #using
Behavioral-based cheating detection in online first person shooters using machine learning techniques (HA, FF, CN), pp. 1–8.
HCIDUXU-WM-2013-GencerBZV #framework #mobile #using
A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques (MG, GB, ÖZ, TV), pp. 651–659.
HCIHCI-III-2013-StorzRMLE #analysis #detection #visualisation #workflow
Annotate. Train. Evaluate. A Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection (MS, MR, RM, HL, ME), pp. 196–205.
CIKMCIKM-2013-Guestrin #scalability #usability
Usability in machine learning at scale with graphlab (CG), pp. 5–6.
ICMLICML-c1-2013-MenonTGLK #framework #programming
A Machine Learning Framework for Programming by Example (AKM, OT, SG, BWL, AK), pp. 187–195.
ICMLICML-c3-2013-GittensM #scalability
Revisiting the Nystrom method for improved large-scale machine learning (AG, MWM), pp. 567–575.
MLDMMLDM-2013-GopalakrishnaOLL #algorithm #metric
Relevance as a Metric for Evaluating Machine Learning Algorithms (AKG, TO, AL, JJL), pp. 195–208.
SEKESEKE-2013-CarrerasZO
A Machine Learning Based File Archival Tool (RC, DZ, JO), pp. 73–76.
ICSEICSE-2013-Jonsson #performance #scalability #using
Increasing anomaly handling efficiency in large organizations using applied machine learning (LJ), pp. 1361–1364.
SACSAC-2013-AkritidisB #algorithm #classification #research
A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.
SACSAC-2013-BerralGT #automation
Empowering automatic data-center management with machine learning (JLB, RG, JT), pp. 170–172.
CASECASE-2013-SharabianiDBCND #predict
Machine learning based prediction of warfarin optimal dosing for African American patients (AS, HD, AB, LC, EN, KD), pp. 623–628.
CCCC-2013-MooreC #automation #generative #policy #using
Automatic Generation of Program Affinity Policies Using Machine Learning (RWM, BRC), pp. 184–203.
CGOCGO-2013-KulkarniCWS #automation #heuristic #using
Automatic construction of inlining heuristics using machine learning (SK, JC, CW, DS), p. 12.
DATEDATE-2013-DeOrioLBB #debugging #detection
Machine learning-based anomaly detection for post-silicon bug diagnosis (AD, QL, MB, VB), pp. 491–496.
SIGMODSIGMOD-2012-LinK #scalability #twitter
Large-scale machine learning at twitter (JL, AK), pp. 793–804.
TPDLTPDL-2012-RathodC
Machine Learning in Building a Collection of Computer Science Course Syllabi (NR, LNC), pp. 357–362.
VLDBVLDB-2012-LowGKBGH #distributed #framework #in the cloud
Distributed GraphLab: A Framework for Machine Learning in the Cloud (YL, JG, AK, DB, CG, JMH), pp. 716–727.
ITiCSEITiCSE-2012-SperlingL #re-engineering #student
Integrating AI and machine learning in software engineering course for high school students (AS, DL), pp. 244–249.
ICPCICPC-2012-Sajnani #approach #architecture #automation
Automatic software architecture recovery: A machine learning approach (HS), pp. 265–268.
AIIDEAIIDE-2012-LeeBL #automation #recommendation
Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative (GL, VB, EAL).
CHICHI-2012-AmershiFW #interactive #named #network #on-demand #social
Regroup: interactive machine learning for on-demand group creation in social networks (SA, JF, DSW), pp. 21–30.
ICMLICML-2012-StorkeyMG
Isoelastic Agents and Wealth Updates in Machine Learning Markets (AJS, JM, KG), p. 133.
ICMLICML-2012-Wagstaff #matter
Machine Learning that Matters (KW), p. 240.
ICPRICPR-2012-VuralA #video
A machine learning system for human-in-the-loop video surveillance (UV, YSA), pp. 1092–1095.
KDDKDD-2012-Lin #case study #data mining #experience #mining
Experiences and lessons in developing industry-strength machine learning and data mining software (CJL), p. 1176.
KDDKDD-2012-ZhouKTX
Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
KDIRKDIR-2012-Dagnino #approach #grid #information management #smarttech
Knowledge Discovery in the Smart Grid — A Machine Learning Approach (AD), pp. 366–369.
MLDMMLDM-2012-ChanguelL #independence #metadata #problem
Content Independent Metadata Production as a Machine Learning Problem (SC, NL), pp. 306–320.
MLDMMLDM-2012-TabatabaeiAKK #classification #internet
Machine Learning-Based Classification of Encrypted Internet Traffic (TST, MA, FK, MK), pp. 578–592.
SEKESEKE-2012-DagninoSR #fault #using
Forecasting Fault Events in Power Distribution Grids Using Machine Learning (AD, KS, LR), pp. 458–463.
SEKESEKE-2012-HaoWZ #classification #empirical
An Empirical Study of Execution-Data Classification Based on Machine Learning (DH, XW, LZ), pp. 283–288.
SIGIRSIGIR-2012-LiX #web
Beyond bag-of-words: machine learning for query-document matching in web search (HL, JX), p. 1177.
SIGIRSIGIR-2012-OzertemCDV #framework #query #ranking
Learning to suggest: a machine learning framework for ranking query suggestions (UO, OC, PD, EV), pp. 25–34.
OOPSLAOOPSLA-2012-KulkarniC #compilation #optimisation #problem #using
Mitigating the compiler optimization phase-ordering problem using machine learning (SK, JC), pp. 147–162.
ICSEICSE-2012-Chioasca #automation #model transformation #requirements #using
Using machine learning to enhance automated requirements model transformation (EVC), pp. 1487–1490.
ICLPICLP-2012-BlockeelBBCP #data mining #mining #modelling #problem
Modeling Machine Learning and Data Mining Problems with FO(·) (HB, BB, MB, BdC, SDP, MD, AL, JR, SV), pp. 14–25.
ICLPICLP-2012-MarateaPR
Applying Machine Learning Techniques to ASP Solving (MM, LP, FR), pp. 37–48.
ICTSSICTSS-2012-StrugS #approach #mutation testing #testing
Machine Learning Approach in Mutation Testing (JS, BS), pp. 200–214.
SMTSMT-2012-AzizWD #estimation #problem #smt
A Machine Learning Technique for Hardness Estimation of QFBV SMT Problems (MAA, AGW, NMD), pp. 57–66.
JCDLJCDL-2011-LearyRWWSM #automation #education
Automating open educational resources assessments: a machine learning generalization study (HL, MR, AEW, PGW, TS, JHM), pp. 283–286.
CoGCIG-2011-AsheSK #data mining #mining #named
Keynotes: Data mining and machine learning applications in MMOs (GA, NRS, JHK).
CoGCIG-2011-GalliLCL #approach #detection #framework
A cheating detection framework for Unreal Tournament III: A machine learning approach (LG, DL, LC, PLL), pp. 266–272.
CHICHI-2011-ChauKHF #interactive #named #network #scalability
Apolo: making sense of large network data by combining rich user interaction and machine learning (DHC, AK, JIH, CF), pp. 167–176.
HCIHCI-MIIE-2011-KarthikP #adaptation #approach #classification #email
Adaptive Machine Learning Approach for Emotional Email Classification (KK, RP), pp. 552–558.
HCIOCSC-2011-PujariK #approach #predict #recommendation
A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
ICEISICEIS-J-2011-Li11f #analysis #approach #case study #type system #using
A Study on Noisy Typing Stream Analysis Using Machine Learning Approach (JL0), pp. 149–161.
CIKMCIKM-2011-QianHCZN #ambiguity
Combining machine learning and human judgment in author disambiguation (YnQ, YH, JC, QZ, ZN), pp. 1241–1246.
ECIRECIR-2011-LeonardLZTCD #data fusion #information retrieval #metric
Applying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval (DL, DL, LZ, FT, RWC, JD), pp. 695–698.
ICMLICML-2011-SujeethLBRCWAOO #domain-specific language #named #parallel
OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning (AKS, HL, KJB, TR, HC, MW, ARA, MO, KO), pp. 609–616.
KDDKDD-2011-ChauKHF #graph #interactive #named #scalability #visualisation
Apolo: interactive large graph sensemaking by combining machine learning and visualization (DHC, AK, JIH, CF), pp. 739–742.
KDDKDD-2011-GhotingKPK #algorithm #data mining #implementation #mining #named #parallel #pipes and filters #tool support
NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce (AG, PK, EPDP, RK), pp. 334–342.
KDDKDD-2011-VijayaraghavanK #data mining #mining #online
Applications of data mining and machine learning in online customer care (RV, PVK), p. 779.
MLDMMLDM-2011-TalbertHT #data mining #framework #mining
A Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage (DAT, MH, ST), pp. 348–361.
SEKESEKE-2011-NoorianBD #classification #framework #testing #towards
Machine Learning-based Software Testing: Towards a Classification Framework (MN, EB, WD), pp. 225–229.
SIGIRSIGIR-2011-LinLJY #approach #query #social
Social annotation in query expansion: a machine learning approach (YL, HL, SJ, ZY), pp. 405–414.
SIGIRSIGIR-2011-ShiYGN #network #recommendation #scalability #social
A large scale machine learning system for recommending heterogeneous content in social networks (YS, DY, AG, SN), pp. 1337–1338.
SIGIRSIGIR-2011-SiJ #information retrieval
Machine learning for information retrieval (LS, RJ), pp. 1293–1294.
SASSAS-2011-NoriR #program analysis
Program Analysis and Machine Learning: A Win-Win Deal (AVN, SKR), pp. 2–3.
ASEASE-2011-ChenHX #approach #evaluation #process
Software process evaluation: A machine learning approach (NC, SCHH, XX), pp. 333–342.
DACDAC-2011-GeQ #multi #using
Dynamic thermal management for multimedia applications using machine learning (YG, QQ), pp. 95–100.
ESOPESOP-2011-BorgstromGGMG #semantics
Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.
EDMEDM-2010-MontalvoBPNG #identification #student #using
Identifying Students’ Inquiry Planning Using Machine Learning (OM, RSJdB, MASP, AN, JDG), pp. 141–150.
ICMLICML-2010-Apte #optimisation
The Role of Machine Learning in Business Optimization (CA), pp. 1–2.
ICMLICML-2010-Raphael #music
Music Plus One and Machine Learning (CR), pp. 21–28.
ICMLICML-2010-ShoebG #detection
Application of Machine Learning To Epileptic Seizure Detection (AHS, JVG), pp. 975–982.
ICPRICPR-2010-Casarrubias-VargasPB #navigation #visual notation
EKF-SLAM and Machine Learning Techniques for Visual Robot Navigation (HCV, APB, EBC), pp. 396–399.
ICPRICPR-2010-ShamiliBA #detection #distributed #mobile #using
Malware Detection on Mobile Devices Using Distributed Machine Learning (ASS, CB, TA), pp. 4348–4351.
KDDKDD-2010-KhoslaCLCHL #approach #predict
An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDIRKDIR-2010-CarulloB #analysis #mining #web
Machine Learning and Link Analysis for Web Content Mining (MC, EB), pp. 156–161.
KMISKMIS-2010-FersiniMTAC #generative #semantics
Semantics and Machine Learning for Building the Next Generation of Judicial Court Management Systems (EF, EM, DT, FA, MC), pp. 51–60.
RecSysRecSys-2010-BenchettaraKR #approach #collaboration #predict #recommendation
A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
SEKESEKE-2010-KhoshgoftaarG #metric #novel #re-engineering #using
Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves (TMK, KG), pp. 203–208.
SIGIRSIGIR-2010-LeeCW #social
Uncovering social spammers: social honeypots + machine learning (KL, JC, SW), pp. 435–442.
ICSEICSE-2010-Cleland-HuangCGE #approach #requirements
A machine learning approach for tracing regulatory codes to product specific requirements (JCH, AC, MG, JE), pp. 155–164.
DATEDATE-2010-HuangSM #fault
Fault diagnosis of analog circuits based on machine learning (KH, HGDS, SM), pp. 1761–1766.
ICSTICST-2010-SilvaJA #cost analysis #execution #symmetry #testing
Machine Learning Methods and Asymmetric Cost Function to Estimate Execution Effort of Software Testing (DGeS, MJ, BTdA), pp. 275–284.
JCDLJCDL-2009-LiC #approach #graph #kernel #predict #recommendation
Recommendation as link prediction: a graph kernel-based machine learning approach (XL, HC), pp. 213–216.
CHICHI-2009-TalbotLKT #classification #interactive #multi #named #visualisation
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers (JT, BL, AK, DST), pp. 1283–1292.
HCIHCI-VAD-2009-BaldirisFMG #adaptation
Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques (SB, RF, CM, SG), pp. 12–20.
HCIHIMI-II-2009-AyodeleZK #approach #email #predict
Email Reply Prediction: A Machine Learning Approach (TA, SZ, RK), pp. 114–123.
ICEISICEIS-DISI-2009-Mao #online
Machine Learning in Online Advertising (JM), p. 27.
CIKMCIKM-2009-SvoreB #approach #retrieval
A machine learning approach for improved BM25 retrieval (KMS, CJCB), pp. 1811–1814.
ICMLICML-2009-BennettBC #information retrieval #summary #tutorial
Tutorial summary: Machine learning in IR: recent successes and new opportunities (PNB, MB, KCT), p. 17.
ICMLICML-2009-BeygelzimerLZ #reduction #summary #tutorial
Tutorial summary: Reductions in machine learning (AB, JL, BZ), p. 12.
ICMLICML-2009-SunJY #problem
A least squares formulation for a class of generalized eigenvalue problems in machine learning (LS, SJ, JY), pp. 977–984.
KDDKDD-2009-JinHS #mining #named #novel #web
OpinionMiner: a novel machine learning system for web opinion mining and extraction (WJ, HHH, RKS), pp. 1195–1204.
MLDMMLDM-2009-SeredinKM #order #set
Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
SEKESEKE-2009-AhsanFW #debugging #estimation #using
Program File Bug Fix Effort Estimation Using Machine Learning Methods for OSS (SNA, JF, FW), pp. 129–134.
SEKESEKE-2009-AxelssonBFSK #code review #detection #fault #interactive #overview #visualisation
Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning (SA, DB, RF, DS, DK), pp. 412–417.
CGOCGO-2009-LeatherBO #automation #compilation #generative #optimisation
Automatic Feature Generation for Machine Learning Based Optimizing Compilation (HL, EVB, MFPO), pp. 81–91.
DATEDATE-2009-WangW
Machine learning-based volume diagnosis (SW, WW), pp. 902–905.
PPoPPPPoPP-2009-WangO #approach #parallel
Mapping parallelism to multi-cores: a machine learning based approach (ZW, MFPO), pp. 75–84.
ICSTSAT-2009-HaimW #using
Restart Strategy Selection Using Machine Learning Techniques (SH, TW), pp. 312–325.
FDGGDCSE-2008-WallaceRM #game studies
Integrating games and machine learning in the undergraduate computer science classroom (SAW, IR, ZM), pp. 56–60.
CHICHI-2008-PatelFLH #development #statistics
Investigating statistical machine learning as a tool for software development (KP, JF, JAL, BLH), pp. 667–676.
ICPRICPR-2008-FerilliBBE #comprehension #documentation #incremental #layout
Incremental machine learning techniques for document layout understanding (SF, MB, TMAB, FE), pp. 1–4.
SEKESEKE-2008-MurphyKHW #testing
Properties of Machine Learning Applications for Use in Metamorphic Testing (CM, GEK, LH, LW), pp. 867–872.
SEKESEKE-2008-Zhang #re-engineering #research
Machine Learning and Value-based Software Engineering: a Research Agenda (DZ), pp. 285–290.
SACSAC-2008-SuKZG #classification #collaboration #using
Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
DACDAC-2008-OzisikyilmazMC #design #performance #using
Efficient system design space exploration using machine learning techniques (, GM, ANC), pp. 966–969.
DATEDATE-2008-KangK #design #framework #manycore #named #optimisation #performance
Magellan: A Search and Machine Learning-based Framework for Fast Multi-core Design Space Exploration and Optimization (SK, RK), pp. 1432–1437.
CoGCIG-2007-FinkDA #behaviour #game studies #using
Extracting NPC behavior from computer games using computer vision and machine learning techniques (AF, JD, JA), pp. 24–31.
HCIHIMI-MTT-2007-CornsML #approach #development #optimisation #using
Development of an Approach for Optimizing the Accuracy of Classifying Claims Narratives Using a Machine Learning Tool (TEXTMINER[4]) (HLC, HRM, MRL), pp. 411–416.
HCIHIMI-MTT-2007-MullerKDCB #human-computer
Machine Learning and Applications for Brain-Computer Interfacing (KRM, MK, GD, GC, BB), pp. 705–714.
ECIRECIR-2007-MoreauCS #automation #query #using
Automatic Morphological Query Expansion Using Analogy-Based Machine Learning (FM, VC, PS), pp. 222–233.
KDDKDD-2007-RaoBFSON #detection #named
LungCAD: a clinically approved, machine learning system for lung cancer detection (RBR, JB, GF, MS, NO, DPN), pp. 1033–1037.
KDDKDD-2007-YanL
Machine learning for stock selection (RJY, CXL), pp. 1038–1042.
MLDMMLDM-2007-ChristiansenD #approach #case study #evaluation #generative #testing
A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders (HC, CMD), pp. 742–755.
MLDMMLDM-2007-SadoddinG #case study #comparative #data mining #detection #mining
A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection (RS, AAG), pp. 404–418.
SEKESEKE-2007-MurphyKA #approach #testing
An Approach to Software Testing of Machine Learning Applications (CM, GEK, MA), p. 167–?.
SACSAC-2007-YingboJJ #approach #workflow
A machine learning approach to semi-automating workflow staff assignment (YL, JW, JS), pp. 340–345.
LCTESLCTES-2007-AbouGhazalehFRXLCMM #cpu #scalability #using
Integrated CPU and l2 cache voltage scaling using machine learning (NA, APF, CR, RX, FL, BRC, DM, RGM), pp. 41–50.
ITiCSEITiCSE-2006-RussellMN #education
Teaching AI through machine learning projects (IR, ZM, TWN), p. 323.
CIKMCIKM-2006-LuPLA #feature model #identification #query
Coupling feature selection and machine learning methods for navigational query identification (YL, FP, XL, NA), pp. 682–689.
ECIRECIR-2006-VittautG #information retrieval #ranking
Machine Learning Ranking for Structured Information Retrieval (JNV, PG), pp. 338–349.
ICPRICPR-v1-2006-Lampert #video
Machine Learning for Video Compression: Macroblock Mode Decision (CHL), pp. 936–940.
ICPRICPR-v1-2006-LiHS #approach #bound #image
A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images (YL, SH, KS), pp. 400–403.
ICPRICPR-v2-2006-CamastraSV #algorithm #benchmark #challenge #metric #pattern matching #pattern recognition #recognition
Offline Cursive Character Challenge: a New Benchmark for Machine Learning and Pattern Recognition Algorithms. (FC, MS, AV), pp. 913–916.
CGOCGO-2006-AgakovBCFFOTTW #optimisation #using
Using Machine Learning to Focus Iterative Optimization (FVA, EVB, JC, BF, GF, MFPO, JT, MT, CKIW), pp. 295–305.
ICDARICDAR-2005-LiuCL #identification #image #using
Language Identification of Character Images Using Machine Learning Techniques (YHL, FC, CCL), pp. 630–634.
ICDARICDAR-2005-SteinkrauSB #algorithm #using
Using GPUs for Machine Learning Algorithms (DS, PYS, IB), pp. 1115–1119.
JCDLJCDL-2005-HuLCMZ #automation #documentation #using
Automatic extraction of titles from general documents using machine learning (YH, HL, YC, DM, QZ), pp. 145–154.
ICSMEICSM-2005-FerencBFL #design pattern #mining
Design Pattern Mining Enhanced by Machine Learning (RF, ÁB, LJF, JL), pp. 295–304.
AIIDEAIIDE-2005-SoutheyXHTB #analysis #automation
Semi-Automated Gameplay Analysis by Machine Learning (FS, GX, RCH, MT, JWB), pp. 123–128.
CoGCIG-2005-ChisholmF #case study #game studies #using
A Study of Machine Learning using the Game of Fox and Geese (KC, DF).
CoGCIG-2005-Pollack #named #research
Nannon: A Nano Backgammon for Machine Learning Research (JBP).
ICEISICEIS-v2-2005-MashechkinPR #anti #approach #enterprise
Enterprise Anti-Spam Solution Based on Machine Learning Approach (IM, MP, AR), pp. 188–193.
CIKMCIKM-2005-CarinoJLWY #mining #web
Mining officially unrecognized side effects of drugs by combining web search and machine learning (CC, YJ, BL, PMW, CTY), pp. 365–372.
CIKMCIKM-2005-NottelmannS #information retrieval #probability
Information retrieval and machine learning for probabilistic schema matching (HN, US), pp. 295–296.
ICMLICML-2005-IresonCCFKL #information management
Evaluating machine learning for information extraction (NI, FC, MEC, DF, NK, AL), pp. 345–352.
ICMLICML-2005-ScholkopfSB #problem
Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.
RERE-2005-AvesaniBPS #requirements #scalability
Facing Scalability Issues in Requirements Prioritization with Machine Learning Techniques (PA, CB, AP, AS), pp. 297–306.
ICSEICSE-2005-Fox #dependence #statistics
Addressing software dependability with statistical and machine learning techniques (AF), p. 8.
CGOCGO-2005-Hind #architecture #virtual machine
Virtual Machine Learning: Thinking like a Computer Architect (MH), p. 11.
JCDLJCDL-2004-EfronEMZ #architecture #scalability
Machine learning for information architecture in a large governmental website (ME, JLE, GM, JZ), pp. 151–159.
ICMLICML-2004-TsochantaridisHJA
Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
KDDKDD-2004-Muslea #online #query
Machine learning for online query relaxation (IM), pp. 246–255.
SEKESEKE-2004-AvesaniBPS #approach #process #requirements
Supporting the Requirements Prioritization Process. A Machine Learning approach (PA, CB, AP, AS), pp. 306–311.
SIGIRSIGIR-2004-ZhangPZ #recognition #using
Focused named entity recognition using machine learning (LZ, YP, TZ), pp. 281–288.
ICSEICSE-2004-BrunE #fault
Finding Latent Code Errors via Machine Learning over Program Executions (YB, MDE), pp. 480–490.
ICDARICDAR-2003-MalerbaEACB #approach #documentation #layout
Correcting the Document Layout: A Machine Learning Approach (DM, FE, OA, MC, MB), p. 97–?.
ITiCSEITiCSE-2003-GeorgiopoulosCWDGGKM #case study #experience
CRCD in machine learning at the University of Central Florida preliminary experiences (MG, JC, ASW, RFD, EG, AJG, MKK, MM), p. 249.
ECIRECIR-2003-ShiEMSLLKO #approach
A Machine Learning Approach for the Curation of Biomedical Literature (MS, DSE, RM, LS, JYKL, HTL, SSK, CJO), pp. 597–604.
ICMLICML-2003-Flach #comprehension #geometry #metric
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
ICMLICML-2003-OngS #kernel
Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.
KDDKDD-2003-FradkinM #random
Experiments with random projections for machine learning (DF, DM), pp. 517–522.
MLDMMLDM-2003-Bunke #data mining #graph #mining #tool support
Graph-Based Tools for Data Mining and Machine Learning (HB), pp. 7–19.
MLDMMLDM-2003-PiwowarskiG #documentation #information retrieval
A Machine Learning Model for Information Retrieval with Structured Documents (BP, PG), pp. 425–438.
SEKESEKE-2003-SpanoudakisGZ #approach #requirements #traceability
Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach (GS, ASdG, AZ), pp. 570–577.
PLDIPLDI-2003-StephensonAMO #compilation #heuristic #optimisation
Meta optimization: improving compiler heuristics with machine learning (MS, SPA, MCM, UMO), pp. 77–90.
PPoPPPPoPP-2003-Puppin #adaptation #convergence #scheduling #using
Adapting convergent scheduling using machine learning (DP), p. 1.
CAiSECAiSE-2002-BerlinM #database #feature model #using
Database Schema Matching Using Machine Learning with Feature Selection (JB, AM), pp. 452–466.
ICPRICPR-v2-2002-Maloof #analysis #on the #statistics #testing
On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
CAVCAV-2002-ClarkeGKS #abstraction #satisfiability #using
SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques (EMC, AG, JHK, OS), pp. 265–279.
ICDARICDAR-2001-NatteeN #classification #comprehension #documentation #geometry #online #using
Geometric Method for Document Understanding and Classification Using On-line Machine Learning (CN, MN), pp. 602–606.
ICEISICEIS-v1-2001-DiazTO #using
A Knowledge-Acquisition Methodology for a Blast Furnace Expert System Using Machine Learning Techniques (ED, JT, FO), pp. 336–339.
ICEISICEIS-v1-2001-SierraRLG #analysis #image #mobile #order #recognition
Machine Learning Approaches for Image Analysis: Recognition of Hand Orders by a Mobile Robot (BS, IR, EL, UG), pp. 330–335.
ICMLICML-2001-DomingosH #algorithm #clustering #scalability
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (PMD, GH), pp. 106–113.
ICMLICML-2000-Hall #feature model
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning (MAH), pp. 359–366.
ICMLICML-2000-KomarekM #adaptation #performance #scalability #set
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (PK, AWM), pp. 495–502.
ICMLICML-2000-Langley
Crafting Papers on Machine Learning (PL), pp. 1207–1216.
ICMLICML-2000-MollPB #problem
Machine Learning for Subproblem Selection (RM, TJP, AGB), pp. 615–622.
ICMLICML-2000-SmolaS #approximate #matrix
Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.
SIGIRSIGIR-2000-ChuangY #approach #summary
Extracting sentence segments for text summarization: a machine learning approach (WTC, JY), pp. 152–159.
SIGIRSIGIR-2000-PetasisCVPKS #adaptation #automation #probability
Automatic adaptation of proper noun dictionaries through cooperation of machine learning and probabilistic methods (GP, AC, PV, GP, VK, CDS), pp. 128–135.
ICMLICML-1998-LiquiereS #graph
Structural Machine Learning with Galois Lattice and Graphs (ML, JS), pp. 305–313.
ASEASE-1998-MaoSL #case study #reuse #usability #using #verification
Reusability Hypothesis Verification using Machine Learning Techniques: A Case Study (YM, HAS, HL), pp. 84–93.
TPDLECDL-1997-SemeraroEMFF #library #online
Machine Learning + On-line Libraries = IDL (GS, FE, DM, NF, SF), pp. 195–214.
ICDARICDAR-1997-AminKS #recognition
Hand Printed Chinese Character Recognition via Machine Learning (AA, SGK, CS), pp. 190–194.
ICDARICDAR-1997-EspositoMSAG #library #semantics
Information Capture and Semantic Indexing of Digital Libraries through Machine Learning Techniques (FE, DM, GS, CDA, GdG), pp. 722–727.
PODSPODS-1997-GunopulosKMT #data mining #mining
Data mining, Hypergraph Transversals, and Machine Learning (DG, RK, HM, HT), pp. 209–216.
HCIHCI-SEC-1997-Moustakis #human-computer #people #question
Do People in HCI Use Machine Learning? (VM), pp. 95–98.
HCIHCI-SEC-1997-Nguifo #interactive
An Interactive Environment for Dynamic Control of Machine Learning Systems (EMN), pp. 31–34.
HCIHCI-SEC-1997-Pohl #modelling #named
LaboUr — Machine Learning for User Modeling (WP), pp. 27–30.
ICMLICML-1997-ZupanBBD #composition
Machine Learning by Function Decomposition (BZ, MB, IB, JD), pp. 421–429.
KDDKDD-1997-BergstenSS #analysis #data mining #mining
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis (UB, JS, PS), pp. 127–130.
KDDKDD-1997-KramerPH #mining
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
ICMLICML-1996-Mannila #data mining #mining
Data Mining and Machine Learning (HM), p. 555.
ICPRICPR-1996-DemsarS #image #using
Using machine learning for content-based image retrieving (JD, FS), pp. 138–142.
KDDKDD-1996-FawcettP #data mining #effectiveness #mining #profiling
Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDDKDD-1996-LakshminarayanHGS #using
Imputation of Missing Data Using Machine Learning Techniques (KL, SAH, RPG, TS), pp. 140–145.
ICDARICDAR-v2-1995-DengelD #approach #classification #clustering #documentation
Clustering and classification of document structure-a machine learning approach (AD, FD), pp. 587–591.
ICDARICDAR-v2-1995-ZiinoAS #recognition #using
Recognition of hand printed Latin characters using machine learning (DZ, AA, CS), pp. 1098–1102.
ICMLICML-1995-Croft #information retrieval
Machine Learning and Information Retrieval (WBC), p. 587.
ICMLICML-1995-SquiresS #automation #recognition
Automatic Speaker Recognition: An Application of Machine Learning (BS, CS), pp. 515–521.
KDDKDD-1995-ChanS #scalability
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning (PKC, SJS), pp. 39–44.
SACSAC-1995-StearnsC #concept #rule-based
Rule-based machine learning of spatial data concepts (SS, DCSC), pp. 242–247.
DLDL-1994-FayyadS #analysis #approach #automation #image #library
The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach (UMF, PS), pp. 225–249.
ICMLICML-1994-DruckerCJCV #algorithm
Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
ICMLICML-1994-Pereira #bias #natural language #problem
Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing — Abstract (FCNP), p. 380.
KDDKDD-1994-AronisP #induction #relational
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning (JMA, FJP), pp. 347–358.
KDDKDD-1994-SasisekharanSW #maintenance #network #using
Proactive Network Maintenance Using Machine Learning (RS, VS, SMW), pp. 453–462.
ASEKBSE-1994-MintonW #source code #using
Using Machine Learning to Synthesize Search Programs (SM, SRW), pp. 31–38.
ICMLICML-1993-FayyadWD #automation #named #scalability
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys (UMF, NW, SGD), pp. 112–119.
SEKESEKE-1993-EspositoMS #information management #refinement
Machine Learning Techniques for Knowledge Acquisition and Refinement (FE, DM, GS), pp. 319–323.
SEKESEKE-1993-WillisP #program transformation #reuse
Machine Learning for Program Transformations in Software Reuse (CPW, DJP), pp. 275–277.
CAiSECAiSE-1992-FouqueV #analysis #approach
Building a Tool for Software Code Analysis: A Machine Learning Approach (GF, CV), pp. 278–289.
ICMLML-1991-ChienWDDFGL #automation
Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ICMLML-1991-ORorkeMABC #evaluation
Machine Learning for Nondestructive Evaluation (PO, SM, MA, WB, DCSC), pp. 620–624.
ICMLML-1991-Thompson #approach #information retrieval
Machine Learning in the Combination of Expert Opinion Approach to IR (PT), pp. 270–274.
ASEKBSE-1991-HarandiL #design #perspective
Acquiring Software Design Schemas: A Machine Learning Perspective (MTH, HYL), pp. 188–197.
ICMLML-1990-Holder #problem
The General Utility Problem in Machine Learning (LBH), pp. 402–410.
SIGIRSIGIR-1990-HalinCK #image #retrieval
Machine Learning and Vectorial Matching for an Image Retrieval Model: EXPRIM and the System RIVAGE (GH, MC, PK), pp. 99–114.
ICMLML-1989-MuggletonBMM #comparison
An Experimental Comparison of Human and Machine Learning Formalisms (SM, MB, JHM, DM), pp. 113–118.
ICMLML-1989-Subramanian
Representational Issues in Machine Learning (DS), pp. 426–429.
ICLPNACLP-1989-MarkovitchS #approach #automation
Automatic Ordering of Subgoals — A Machine Learning Approach (SM, PDS), pp. 224–240.
SIGIRSIGIR-1986-WongZ #approach #information retrieval
A Machine Learning Approach to Information Retrieval (SKMW, WZ), pp. 228–233.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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